Overview

Dataset statistics

Number of variables13
Number of observations800
Missing cells386
Missing cells (%)3.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory194.7 KiB
Average record size in memory249.3 B

Variable types

Numeric9
Text1
Categorical2
Boolean1

Alerts

# is highly overall correlated with Type 2 and 1 other fieldsHigh correlation
Attack is highly overall correlated with Total and 2 other fieldsHigh correlation
Defense is highly overall correlated with Total and 3 other fieldsHigh correlation
Generation is highly overall correlated with # and 1 other fieldsHigh correlation
HP is highly overall correlated with Total and 2 other fieldsHigh correlation
Sp. Atk is highly overall correlated with Total and 3 other fieldsHigh correlation
Sp. Def is highly overall correlated with Total and 2 other fieldsHigh correlation
Speed is highly overall correlated with Total and 1 other fieldsHigh correlation
Total is highly overall correlated with HP and 6 other fieldsHigh correlation
Type 1 is highly overall correlated with Type 2High correlation
Type 2 is highly overall correlated with # and 2 other fieldsHigh correlation
Legendary is highly overall correlated with Total and 1 other fieldsHigh correlation
Legendary is highly imbalanced (59.3%)Imbalance
Type 2 has 386 (48.2%) missing valuesMissing
# is uniformly distributedUniform
Name has unique valuesUnique

Reproduction

Analysis started2023-11-27 16:21:38.267677
Analysis finished2023-11-27 16:22:11.790472
Duration33.52 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

#
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct721
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362.81375
Minimum1
Maximum721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:12.028141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.95
Q1184.75
median364.5
Q3539.25
95-th percentile689.05
Maximum721
Range720
Interquartile range (IQR)354.5

Descriptive statistics

Standard deviation208.3438
Coefficient of variation (CV)0.57424449
Kurtosis-1.1657051
Mean362.81375
Median Absolute Deviation (MAD)177.5
Skewness-0.0011225028
Sum290251
Variance43407.138
MonotonicityIncreasing
2023-11-27T16:22:12.415513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
479 6
 
0.8%
711 4
 
0.5%
710 4
 
0.5%
386 4
 
0.5%
413 3
 
0.4%
150 3
 
0.4%
646 3
 
0.4%
6 3
 
0.4%
310 2
 
0.2%
308 2
 
0.2%
Other values (711) 766
95.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 2
0.2%
4 1
 
0.1%
5 1
 
0.1%
6 3
0.4%
7 1
 
0.1%
8 1
 
0.1%
9 2
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
721 1
0.1%
720 2
0.2%
719 2
0.2%
718 1
0.1%
717 1
0.1%
716 1
0.1%
715 1
0.1%
714 1
0.1%
713 1
0.1%
712 1
0.1%

Name
Text

UNIQUE 

Distinct800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size51.7 KiB
2023-11-27T16:22:12.756742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length25
Median length23
Mean length8.84125
Min length3

Characters and Unicode

Total characters7073
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique800 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowVenusaurMega Venusaur
5th rowCharmander
ValueCountFrequency (%)
forme 21
 
2.3%
size 8
 
0.9%
rotom 6
 
0.7%
cloak 3
 
0.3%
mewtwo 3
 
0.3%
kyurem 3
 
0.3%
charizard 3
 
0.3%
sharpedo 2
 
0.2%
medicham 2
 
0.2%
kangaskhan 2
 
0.2%
Other values (796) 846
94.1%
2023-11-27T16:22:13.598373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 682
 
9.6%
e 644
 
9.1%
o 571
 
8.1%
r 509
 
7.2%
i 469
 
6.6%
n 378
 
5.3%
l 371
 
5.2%
t 319
 
4.5%
u 248
 
3.5%
s 220
 
3.1%
Other values (53) 2662
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5969
84.4%
Uppercase Letter 994
 
14.1%
Space Separator 99
 
1.4%
Other Punctuation 4
 
0.1%
Decimal Number 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 682
11.4%
e 644
10.8%
o 571
 
9.6%
r 509
 
8.5%
i 469
 
7.9%
n 378
 
6.3%
l 371
 
6.2%
t 319
 
5.3%
u 248
 
4.2%
s 220
 
3.7%
Other values (17) 1558
26.1%
Uppercase Letter
ValueCountFrequency (%)
S 137
13.8%
M 126
12.7%
C 65
 
6.5%
G 65
 
6.5%
P 59
 
5.9%
A 55
 
5.5%
F 50
 
5.0%
B 49
 
4.9%
D 48
 
4.8%
L 46
 
4.6%
Other values (16) 294
29.6%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
' 1
25.0%
% 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
5 1
33.3%
0 1
33.3%
Other Symbol
ValueCountFrequency (%)
♀ 1
50.0%
♂ 1
50.0%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6963
98.4%
Common 110
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 682
 
9.8%
e 644
 
9.2%
o 571
 
8.2%
r 509
 
7.3%
i 469
 
6.7%
n 378
 
5.4%
l 371
 
5.3%
t 319
 
4.6%
u 248
 
3.6%
s 220
 
3.2%
Other values (43) 2552
36.7%
Common
ValueCountFrequency (%)
99
90.0%
. 2
 
1.8%
- 2
 
1.8%
♀ 1
 
0.9%
♂ 1
 
0.9%
' 1
 
0.9%
2 1
 
0.9%
5 1
 
0.9%
0 1
 
0.9%
% 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7069
99.9%
None 2
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 682
 
9.6%
e 644
 
9.1%
o 571
 
8.1%
r 509
 
7.2%
i 469
 
6.6%
n 378
 
5.3%
l 371
 
5.2%
t 319
 
4.5%
u 248
 
3.5%
s 220
 
3.1%
Other values (50) 2658
37.6%
None
ValueCountFrequency (%)
é 2
100.0%
Misc Symbols
ValueCountFrequency (%)
♀ 1
50.0%
♂ 1
50.0%

Type 1
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
Water
112 
Normal
98 
Grass
70 
Bug
69 
Psychic
57 
Other values (13)
394 

Length

Max length8
Median length7
Mean length5.26
Min length3

Characters and Unicode

Total characters4208
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowGrass
5th rowFire

Common Values

ValueCountFrequency (%)
Water 112
14.0%
Normal 98
12.2%
Grass 70
 
8.8%
Bug 69
 
8.6%
Psychic 57
 
7.1%
Fire 52
 
6.5%
Rock 44
 
5.5%
Electric 44
 
5.5%
Ground 32
 
4.0%
Ghost 32
 
4.0%
Other values (8) 190
23.8%

Length

2023-11-27T16:22:14.131702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water 112
14.0%
normal 98
12.2%
grass 70
 
8.8%
bug 69
 
8.6%
psychic 57
 
7.1%
fire 52
 
6.5%
rock 44
 
5.5%
electric 44
 
5.5%
ground 32
 
4.0%
ghost 32
 
4.0%
Other values (8) 190
23.8%

Most occurring characters

ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3408
81.0%
Uppercase Letter 800
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 488
14.3%
a 360
10.6%
o 294
8.6%
e 286
8.4%
c 270
7.9%
s 257
7.5%
i 256
7.5%
t 242
 
7.1%
l 173
 
5.1%
g 159
 
4.7%
Other values (7) 623
18.3%
Uppercase Letter
ValueCountFrequency (%)
G 134
16.8%
W 112
14.0%
F 100
12.5%
N 98
12.2%
P 85
10.6%
B 69
8.6%
D 63
7.9%
R 44
 
5.5%
E 44
 
5.5%
S 27
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Type 2
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)4.3%
Missing386
Missing (%)48.2%
Memory size40.5 KiB
Flying
97 
Ground
35 
Poison
34 
Psychic
33 
Fighting
26 
Other values (13)
189 

Length

Max length8
Median length7
Mean length5.6521739
Min length3

Characters and Unicode

Total characters2340
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoison
2nd rowPoison
3rd rowPoison
4th rowPoison
5th rowFlying

Common Values

ValueCountFrequency (%)
Flying 97
 
12.1%
Ground 35
 
4.4%
Poison 34
 
4.2%
Psychic 33
 
4.1%
Fighting 26
 
3.2%
Grass 25
 
3.1%
Fairy 23
 
2.9%
Steel 22
 
2.8%
Dark 20
 
2.5%
Dragon 18
 
2.2%
Other values (8) 81
 
10.1%
(Missing) 386
48.2%

Length

2023-11-27T16:22:14.464219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying 97
23.4%
ground 35
 
8.5%
poison 34
 
8.2%
psychic 33
 
8.0%
fighting 26
 
6.3%
grass 25
 
6.0%
fairy 23
 
5.6%
steel 22
 
5.3%
dark 20
 
4.8%
dragon 18
 
4.3%
Other values (8) 81
19.6%

Most occurring characters

ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1926
82.3%
Uppercase Letter 414
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 257
13.3%
n 210
10.9%
g 170
8.8%
r 157
8.2%
y 153
7.9%
o 153
7.9%
s 131
 
6.8%
l 129
 
6.7%
c 106
 
5.5%
a 104
 
5.4%
Other values (7) 356
18.5%
Uppercase Letter
ValueCountFrequency (%)
F 158
38.2%
G 74
17.9%
P 67
16.2%
D 38
 
9.2%
S 22
 
5.3%
I 14
 
3.4%
R 14
 
3.4%
W 14
 
3.4%
E 6
 
1.4%
N 4
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.1025
Minimum180
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:15.269209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1330
median450
Q3515
95-th percentile630
Maximum780
Range600
Interquartile range (IQR)185

Descriptive statistics

Standard deviation119.96304
Coefficient of variation (CV)0.27571214
Kurtosis-0.50746071
Mean435.1025
Median Absolute Deviation (MAD)85
Skewness0.15252992
Sum348082
Variance14391.131
MonotonicityNot monotonic
2023-11-27T16:22:15.710758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 37
 
4.6%
405 26
 
3.2%
500 23
 
2.9%
580 23
 
2.9%
300 19
 
2.4%
490 18
 
2.2%
525 16
 
2.0%
495 15
 
1.9%
480 15
 
1.9%
330 15
 
1.9%
Other values (190) 593
74.1%
ValueCountFrequency (%)
180 1
 
0.1%
190 1
 
0.1%
194 1
 
0.1%
195 3
0.4%
198 1
 
0.1%
200 3
0.4%
205 5
0.6%
210 3
0.4%
213 1
 
0.1%
215 1
 
0.1%
ValueCountFrequency (%)
780 3
 
0.4%
770 2
 
0.2%
720 1
 
0.1%
700 9
1.1%
680 13
1.6%
670 4
 
0.5%
660 1
 
0.1%
640 1
 
0.1%
635 1
 
0.1%
634 2
 
0.2%

HP
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.25875
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:16.228121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.95
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.534669
Coefficient of variation (CV)0.3686851
Kurtosis7.2320784
Mean69.25875
Median Absolute Deviation (MAD)15
Skewness1.5682244
Sum55407
Variance652.01932
MonotonicityNot monotonic
2023-11-27T16:22:16.710794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 67
 
8.4%
50 63
 
7.9%
70 57
 
7.1%
65 46
 
5.8%
75 43
 
5.4%
80 43
 
5.4%
45 38
 
4.8%
40 38
 
4.8%
55 37
 
4.6%
100 32
 
4.0%
Other values (84) 336
42.0%
ValueCountFrequency (%)
1 1
 
0.1%
10 1
 
0.1%
20 6
 
0.8%
25 2
 
0.2%
28 1
 
0.1%
30 13
1.6%
31 1
 
0.1%
35 15
1.9%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
255 1
 
0.1%
250 1
 
0.1%
190 1
 
0.1%
170 1
 
0.1%
165 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
144 1
 
0.1%
140 1
 
0.1%
135 1
 
0.1%

Attack
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.00125
Minimum5
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:17.037907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q3100
95-th percentile136.2
Maximum190
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.457366
Coefficient of variation (CV)0.41084623
Kurtosis0.16971731
Mean79.00125
Median Absolute Deviation (MAD)20
Skewness0.55161375
Sum63201
Variance1053.4806
MonotonicityNot monotonic
2023-11-27T16:22:17.431795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 40
 
5.0%
65 39
 
4.9%
50 37
 
4.6%
80 37
 
4.6%
60 33
 
4.1%
85 33
 
4.1%
75 32
 
4.0%
70 31
 
3.9%
55 30
 
3.8%
90 30
 
3.8%
Other values (101) 458
57.2%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 1
 
0.1%
20 8
1.0%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 7
0.9%
27 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
190 1
 
0.1%
185 1
 
0.1%
180 3
 
0.4%
170 2
 
0.2%
165 3
 
0.4%
164 1
 
0.1%
160 5
0.6%
155 2
 
0.2%
150 11
1.4%
147 1
 
0.1%

Defense
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.8425
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:17.968145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median70
Q390
95-th percentile130
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.183501
Coefficient of variation (CV)0.42229747
Kurtosis2.7262604
Mean73.8425
Median Absolute Deviation (MAD)20
Skewness1.1559123
Sum59074
Variance972.41071
MonotonicityNot monotonic
2023-11-27T16:22:18.464418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 54
 
6.8%
50 49
 
6.1%
60 46
 
5.8%
80 39
 
4.9%
40 36
 
4.5%
65 36
 
4.5%
90 35
 
4.4%
100 33
 
4.1%
55 32
 
4.0%
45 32
 
4.0%
Other values (93) 408
51.0%
ValueCountFrequency (%)
5 2
 
0.2%
10 1
 
0.1%
15 4
 
0.5%
20 4
 
0.5%
23 1
 
0.1%
25 2
 
0.2%
28 1
 
0.1%
30 14
1.8%
32 2
 
0.2%
33 1
 
0.1%
ValueCountFrequency (%)
230 3
0.4%
200 2
 
0.2%
184 1
 
0.1%
180 3
0.4%
168 1
 
0.1%
160 3
0.4%
150 7
0.9%
145 2
 
0.2%
140 6
0.8%
135 2
 
0.2%

Sp. Atk
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.82
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:18.899422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q149.75
median65
Q395
95-th percentile131.05
Maximum194
Range184
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation32.722294
Coefficient of variation (CV)0.44935861
Kurtosis0.29789366
Mean72.82
Median Absolute Deviation (MAD)20
Skewness0.7446625
Sum58256
Variance1070.7485
MonotonicityNot monotonic
2023-11-27T16:22:19.286157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 51
 
6.4%
40 49
 
6.1%
65 44
 
5.5%
50 39
 
4.9%
55 35
 
4.4%
45 33
 
4.1%
70 30
 
3.8%
35 29
 
3.6%
80 27
 
3.4%
95 27
 
3.4%
Other values (95) 436
54.5%
ValueCountFrequency (%)
10 3
 
0.4%
15 4
 
0.5%
20 8
 
1.0%
23 1
 
0.1%
24 2
 
0.2%
25 11
1.4%
27 2
 
0.2%
29 1
 
0.1%
30 24
3.0%
31 1
 
0.1%
ValueCountFrequency (%)
194 1
 
0.1%
180 3
 
0.4%
175 1
 
0.1%
170 3
 
0.4%
165 2
 
0.2%
160 2
 
0.2%
159 1
 
0.1%
154 2
 
0.2%
150 9
1.1%
145 4
0.5%

Sp. Def
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.9025
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:19.647515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile32.95
Q150
median70
Q390
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.828916
Coefficient of variation (CV)0.38703683
Kurtosis1.6283941
Mean71.9025
Median Absolute Deviation (MAD)20
Skewness0.85401861
Sum57522
Variance774.44855
MonotonicityNot monotonic
2023-11-27T16:22:20.031978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 52
 
6.5%
50 50
 
6.2%
55 47
 
5.9%
65 44
 
5.5%
60 43
 
5.4%
70 40
 
5.0%
75 40
 
5.0%
90 36
 
4.5%
45 35
 
4.4%
40 30
 
3.8%
Other values (82) 383
47.9%
ValueCountFrequency (%)
20 6
 
0.8%
23 1
 
0.1%
25 11
1.4%
30 20
2.5%
31 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
35 18
2.2%
36 1
 
0.1%
ValueCountFrequency (%)
230 1
 
0.1%
200 1
 
0.1%
160 2
 
0.2%
154 3
 
0.4%
150 7
0.9%
140 2
 
0.2%
138 1
 
0.1%
135 4
0.5%
130 9
1.1%
129 1
 
0.1%

Speed
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.2775
Minimum5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:20.376616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q390
95-th percentile115
Maximum180
Range175
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.060474
Coefficient of variation (CV)0.42562299
Kurtosis-0.23643667
Mean68.2775
Median Absolute Deviation (MAD)21
Skewness0.3579333
Sum54622
Variance844.51113
MonotonicityNot monotonic
2023-11-27T16:22:20.722866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 46
 
5.8%
60 44
 
5.5%
70 37
 
4.6%
65 36
 
4.5%
30 35
 
4.4%
80 33
 
4.1%
40 32
 
4.0%
100 31
 
3.9%
90 31
 
3.9%
55 30
 
3.8%
Other values (98) 445
55.6%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 9
1.1%
20 15
1.9%
22 1
 
0.1%
23 4
 
0.5%
24 1
 
0.1%
25 10
1.2%
28 4
 
0.5%
29 3
 
0.4%
ValueCountFrequency (%)
180 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
145 3
0.4%
140 2
 
0.2%
135 2
 
0.2%
130 6
0.8%
128 1
 
0.1%
127 1
 
0.1%
126 1
 
0.1%

Generation
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.32375
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2023-11-27T16:22:21.030177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6612904
Coefficient of variation (CV)0.49982411
Kurtosis-1.2395758
Mean3.32375
Median Absolute Deviation (MAD)2
Skewness0.0142581
Sum2659
Variance2.7598858
MonotonicityIncreasing
2023-11-27T16:22:21.323299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 166
20.8%
5 165
20.6%
3 160
20.0%
4 121
15.1%
2 106
13.2%
6 82
10.2%
ValueCountFrequency (%)
1 166
20.8%
2 106
13.2%
3 160
20.0%
4 121
15.1%
5 165
20.6%
6 82
10.2%
ValueCountFrequency (%)
6 82
10.2%
5 165
20.6%
4 121
15.1%
3 160
20.0%
2 106
13.2%
1 166
20.8%

Legendary
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size928.0 B
False
735 
True
 
65
ValueCountFrequency (%)
False 735
91.9%
True 65
 
8.1%
2023-11-27T16:22:21.614784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Interactions

2023-11-27T16:22:08.170193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:44.503080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:48.214652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:51.395249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:53.960942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:57.117092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:59.906503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:02.821146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:05.573064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:08.475282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:44.998844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:48.561861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:51.671118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:54.290099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:57.441809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:00.550753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:03.136990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:05.833063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:08.747235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:45.317688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:48.898959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:52.041681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:54.531558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:57.767720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:00.895257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:03.383794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:06.039155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:09.100085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:45.741716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:49.277113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:52.345307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:54.800244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:57.947589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:01.204521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:03.652446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:06.256828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:09.409971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:46.176130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:49.591770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:52.732555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:55.138636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:58.261254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:01.496595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:04.011456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:06.516109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:09.742389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:46.532475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:49.960882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:52.993808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:55.686375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:58.637841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:01.732428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:04.281909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:06.831339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:10.029787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:46.947906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:50.318205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:53.322487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:56.117859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:58.949800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:02.004748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:04.586715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:07.166425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:10.343536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:47.437838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:50.671711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:53.520387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:56.353854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:59.210021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:02.299491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:04.953506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:07.481832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:10.592320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:47.842403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:51.055529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:53.760298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:56.729135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:21:59.553393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:02.576844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:05.275548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T16:22:07.825287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-27T16:22:21.807211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
#AttackDefenseGenerationHPLegendarySp. AtkSp. DefSpeedTotalType 1Type 2
#1.0000.1030.1170.9840.1200.2590.0880.0760.0190.1220.1620.265
Attack0.1031.0000.5150.0540.5660.3660.3620.3210.3730.7200.1190.105
Defense0.1170.5151.0000.0580.4330.2740.3140.5790.0930.6820.1510.147
Generation0.9840.0540.0581.0000.0820.0780.0390.019-0.0140.0540.1580.282
HP0.1200.5660.4330.0821.0000.3570.4710.4930.2660.7130.0770.144
Legendary0.2590.3660.2740.0780.3571.0000.3730.3290.3130.4440.3030.135
Sp. Atk0.0880.3620.3140.0390.4710.3731.0000.5720.4600.7300.1490.070
Sp. Def0.0760.3210.5790.0190.4930.3290.5721.0000.3210.7570.0820.097
Speed0.0190.3730.093-0.0140.2660.3130.4600.3211.0000.5680.1300.139
Total0.1220.7200.6820.0540.7130.4440.7300.7570.5681.0000.1290.119
Type 10.1620.1190.1510.1580.0770.3030.1490.0820.1300.1291.0000.244
Type 20.2650.1050.1470.2820.1440.1350.0700.0970.1390.1190.2441.000
2023-11-27T16:22:22.104726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
#Type 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
#1.0000.3970.5790.3730.1570.2320.2980.2120.1840.2250.9660.340
Type 10.3971.0000.7300.3280.2280.3010.3560.3680.2390.2980.3770.388
Type 20.5790.7301.0000.3030.3490.2780.3660.1850.2140.3890.6040.175
Total0.3730.3280.3031.0000.5610.8010.6740.8370.5830.5980.1780.923
HP0.1570.2280.3490.5611.0000.4960.5010.3430.5760.2020.0850.359
Attack0.2320.3010.2780.8010.4961.0000.5740.6550.2980.4510.1340.478
Defense0.2980.3560.3660.6740.5010.5741.0000.3050.6360.2220.1480.348
Sp. Atk0.2120.3680.1850.8370.3430.6550.3051.0000.4640.5550.0550.649
Sp. Def0.1840.2390.2140.5830.5760.2980.6360.4641.0000.3040.1570.390
Speed0.2250.2980.3890.5980.2020.4510.2220.5550.3041.0000.1520.450
Generation0.9660.3770.6040.1780.0850.1340.1480.0550.1570.1521.0000.109
Legendary0.3400.3880.1750.9230.3590.4780.3480.6490.3900.4500.1091.000
2023-11-27T16:22:22.705460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
LegendaryType 1Type 2
Legendary1.0000.3030.135
Type 10.3031.0000.244
Type 20.1350.2441.000

Missing values

2023-11-27T16:22:10.975806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-27T16:22:11.581376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
01BulbasaurGrassPoison3184549496565451False
12IvysaurGrassPoison4056062638080601False
23VenusaurGrassPoison525808283100100801False
33VenusaurMega VenusaurGrassPoison62580100123122120801False
44CharmanderFireNaN3093952436050651False
55CharmeleonFireNaN4055864588065801False
66CharizardFireFlying534788478109851001False
76CharizardMega Charizard XFireDragon63478130111130851001False
86CharizardMega Charizard YFireFlying63478104781591151001False
97SquirtleWaterNaN3144448655064431False
#NameType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendary
790714NoibatFlyingDragon2454030354540556False
791715NoivernFlyingDragon53585708097801236False
792716XerneasFairyNaN6801261319513198996True
793717YveltalDarkFlying6801261319513198996True
794718Zygarde50% FormeDragonGround6001081001218195956True
795719DiancieRockFairy60050100150100150506True
796719DiancieMega DiancieRockFairy700501601101601101106True
797720HoopaHoopa ConfinedPsychicGhost6008011060150130706True
798720HoopaHoopa UnboundPsychicDark6808016060170130806True
799721VolcanionFireWater6008011012013090706True